activity
20192021
most citedCompositional generalization in a deep seq2seq model by separating syntax and semantics

85 citations · 108 across the 4 of their papers we have counts for

collaborators

5 papers

q-bio.NC2021

The Structure of Systematicity in the Brain

Randall C. O'Reilly, Charan Ranganath, Jacob L. Russin

A hallmark of human intelligence is the ability to adapt to new situations, by applying learned rules to new content (systematicity) and thereby enabling an open-ended number of in…

cs.LG202121 cited

Compositional Processing Emerges in Neural Networks Solving Math Problems

Jacob Russin, Roland Fernandez, Hamid Palangi +4

A longstanding question in cognitive science concerns the learning mechanisms underlying compositionality in human cognition. Humans can infer the structured relationships (e.g., g…

q-bio.NC20212 cited

Complementary Structure-Learning Neural Networks for Relational Reasoning

Jacob Russin, Maryam Zolfaghar, Seongmin A. Park +2

The neural mechanisms supporting flexible relational inferences, especially in novel situations, are a major focus of current research. In the complementary learning systems framew…

q-bio.NC2020

Deep Predictive Learning in Neocortex and Pulvinar

Randall C. O'Reilly, Jacob L. Russin, Maryam Zolfaghar +1

How do humans learn from raw sensory experience? Throughout life, but most obviously in infancy, we learn without explicit instruction. We propose a detailed biological mechanism f…

cs.LG201985 cited

Compositional generalization in a deep seq2seq model by separating syntax and semantics

Jake Russin, Jason Jo, Randall C. O'Reilly +1

Standard methods in deep learning for natural language processing fail to capture the compositional structure of human language that allows for systematic generalization outside of…